Here is a sample output:

Quantile Regression Analysis of Deviance Table

Model: op ~ inp1 + inp2 + inp3 + inp4 + inp5 + inp6 + inp7 + inp8 + inp9
Joint Test of Equality of Slopes: tau in {  0.15 0.3  }

  Df Resid Df F value Pr(>F)
1  9     1337  0.5256 0.8568

Warning messages:
1: In summary.rq(x, se = "nid", covariance = TRUE) : 93 non-positive fis
2: In summary.rq(x, se = "nid", covariance = TRUE) : 138 non-positive fis

How to interpret the above results?? Does the anova() function give the best model, for tau=0.15 vs. tau=0.3?

  • 1
    $\begingroup$ I think it would help to tell people what fit1 and fit2 are $\endgroup$
    – Peter Flom
    Commented Dec 5, 2011 at 10:48

1 Answer 1


The interpretation of the result of a joint test of equality of slopes is that overall, the effect of the entire set of coefficients is uniform across quantiles.

Understand that this is not a test of the performance of your two models, it simply tests whether slope coefficients of those models, from several quantiles, can be considered not different.


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